Year: 2025
Type: conference-paper
Abstract: Arabic exhibits a rich and intricate linguistic landscape, with Modern Standard Arabic (MSA) serving as the formal written and spoken medium, alongside a wide variety of regional dialects used in everyday communication.These dialects vary considerably in syntax, vocabulary, phonology, and meaning, presenting significant challenges for natural language processing (NLP).The complexity is particularly pronounced in sentiment analysis, where emotional expressions and idiomatic phrases differ markedly across regions, hindering consistent and accurate sentiment detection.This paper describes our submission to the Ahasis Shared Task: A Benchmark for Arabic Sentiment Analysis in the hospitality domain.This shared task focuses on advancing sentiment analysis techniques for Arabic dialects in the hotel domain.Our proposed approach achieved an F1 score of 0.88 % on the internal test set (split from the original training data), and 79.16% on the official hidden test set of the shared task.This performance secured our team second place in the Ahasis Shared Task.
Authors: Mustapha Jaballah, Dhaou Ghoul, Ammar Mars
Institutions: Tunis University, ENSIT
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7140074147